Open Source and the Data Lakehouse - Understanding Components and Technologies

Overview

Discover how data lakehouses combine performance and affordability, exploring Apache Arrow, Iceberg, and Project Nessie as alternatives to traditional data warehouses.

Syllabus

• Introduction to Data Lakehouses
Definition and key characteristics
Comparison with data warehouses and data lakes
Benefits and limitations of data lakehouses
• Core Components of Data Lakehouses
Storage and compute separation
Metadata management
Query engines and optimization
• Apache Arrow
Overview of Apache Arrow
In-memory columnar format
Performance benefits for data lakehouses
Integration with other data technologies
• Apache Iceberg
Introduction to Apache Iceberg
Architecture and features
Advantages over traditional table formats
Use cases and implementation examples
• Project Nessie
Overview of Project Nessie
Version control for data lakehouses
Branching, merging, and reproducibility
Ecosystem and integration
• Comparing Open Source Data Lakehouse Technologies
Use cases and performance comparisons
Cost and affordability analysis
Case studies of successful implementations
• Practical Considerations and Best Practices
Data governance and security
Performance optimization strategies
Choosing the right components for specific needs
• Future Trends and Developments in Data Lakehouses
Emerging technologies and innovations
Industry adoption and evolution
Speculations on future directions in data management
• Course Review and Final Thoughts
Recap of key concepts and technologies
Discussion on the impact of data lakehouses in the industry
Q&A and interactive discussions

Subjects

Business